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Spatial factors influencing building age prediction and implications for urban residential energy modelling

Garbasevschi, Oana M. and Estevam Schmiedt, Jacob and Verma, Trivik and Lefter, Iulia and Korthals Altes, Willem K. and Droin, Ariane and Schiricke, Björn and Wurm, Michael (2021) Spatial factors influencing building age prediction and implications for urban residential energy modelling. Computers, Environment and Urban Systems, 88, p. 101637. Elsevier. doi: 10.1016/j.compenvurbsys.2021.101637. ISSN 0198-9715.

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Official URL: http://dx.doi.org/10.1016/j.compenvurbsys.2021.101637

Abstract

Urban energy consumption is expected to continuously increase alongside rapid urbanization. The building sector represents a key area for curbing the consumption trend and reducing energy-related emissions by adopting energy efficiency strategies. Building age acts as a proxy for building insulation properties and is an important parameter for energy models that facilitate decision making. The present study explores the potential of predicting residential building age at a large geographical scale from open spatial data sources in eight municipalities in the German federal state of North-Rhine Westphalia. The proposed framework combines building attributes with street and block metrics as classification features in a Random Forest model. Results show that the addition of urban fabric metrics improves the accuracy of building age prediction in specific training scenarios. Furthermore, the findings highlight the way in which the spatial disposition of training and test samples influences classification accuracy. Additionally, the paper investigates the impact of age misclassification on residential building heat demand estimation. The age classification model leads to reasonable errors in energy estimates, in various scenarios of training, which suggests that the proposed method is a promising addition to the urban energy modelling toolkit.

Item URL in elib:https://elib.dlr.de/141993/
Document Type:Article
Title:Spatial factors influencing building age prediction and implications for urban residential energy modelling
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Garbasevschi, Oana M.mihaela.garbasevschi (at) dlr.dehttps://orcid.org/0000-0003-1175-883X
Estevam Schmiedt, JacobJacob.EstevamSchmiedt (at) dlr.dehttps://orcid.org/0000-0002-0794-6769
Verma, TrivikT.Verma (at) tudelft.nlhttps://orcid.org/0000-0003-2761-5155
Lefter, IuliaI.Lefter (at) tudelft.nlUNSPECIFIED
Korthals Altes, Willem K.W.K.KorthalsAltes (at) tudelft.nlhttps://orcid.org/0000-0002-2745-0007
Droin, ArianeAriane.Droin (at) dlr.deUNSPECIFIED
Schiricke, BjörnBjoern.Schiricke (at) dlr.dehttps://orcid.org/0000-0003-0572-2048
Wurm, Michaelmichael.wurm (at) dlr.dehttps://orcid.org/0000-0001-5967-1894
Date:25 April 2021
Journal or Publication Title:Computers, Environment and Urban Systems
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:88
DOI :10.1016/j.compenvurbsys.2021.101637
Page Range:p. 101637
Editors:
EditorsEmailEditor's ORCID iD
Grubesic, T.H.grubesic@asu.edu0000-0003-4517-586X
Publisher:Elsevier
ISSN:0198-9715
Status:Published
Keywords:Open data, Urban morphology, Spatial autocorrelation, Residential building age, Residential heat demand, Random forest
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:High-Temperature Thermal Technologies
DLR - Research area:Energy
DLR - Program:E SW - Solar and Wind Energy
DLR - Research theme (Project):E - Condition Monitoring, R - Remote Sensing and Geo Research
Location: Jülich , Oberpfaffenhofen
Institutes and Institutions:Institute of Solar Research > Qualification
German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Garbasevschi, Oana Mihaela
Deposited On:10 May 2021 10:54
Last Modified:10 May 2021 10:54

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